Artificial intelligence for wireless network analysis

ABSTRACT

This invention relates to using artificial intelligence for analyzing real-life collected data from an operation system, modeling the collected data to identify characteristics of events, analyzing the models to conclude an optimal solution for maximizing the performance of the operation system.

RELATED APPLICATION

The present invention is a continuation application of and claimspriority to the U.S. patent application Ser. No. 11/420,879, filed onMay 30, 2006 now U.S. Pat. No. 7,512,570, and is herein incorporated inits entirety by reference for all purposes.

FIELD OF INVENTION

This invention relates to analyzing real-life collected data by usingvarious modeling algorithms to generate various aspects of models. Thesemodels represent different characteristics of behavior from where thereal-life data were collected. Further, these models are analyzed byvarious analysis tools to conclude an optimum expectation. The optimumexpectation can be dynamically refined by adjusting constraints so thatan optimum solution can be obtained in order to meet the real-lifebehavior needs.

BACKGROUND OF THE INVENTION

Information processing has been an important technology in our dailylife. It determines what we do, how we do, when we do, where we do, andwho to do for anything in our life. Due to technologies development, theinformation processing speed has been faster and faster. The outcome ofthe processed information are various data. The human intelligence hasbeen proved for efficiently handling and analyzing many processed datain order to determine an optimum solution. However, in nowadays, theamount of data that are generated by the modern technologies and systemshas been far more beyond the human being's capability to analyze anddetermine an optimum solution. Therefore, we have to rely on thetechnology of artificial intelligence for consolidating essentialintelligence, and the technology of high processing speed of a system toconclude an optimum solution in order to achieve a desired performancerequirement.

SUMMARY OF THE INVENTION

This invention is an artificial intelligence analyzer and generator forcollecting, partitioning, modeling, and analyzing the massive data thatare generated from time to time in our daily life. All of the data arerelated in a sense that one may affect the other. Further, eachrepresenting data may also bear different “weight” in affecting theothers. Therefore, complex relationships are established between thesedata. For example, when fifty grocery shoppers checking out at the sametime, a “ten casher checkout lines” store and a “five casher checkoutlines” store make the customer waiting time for checkout are different.Therefore, when the manager of the “five casher checkout line” storeneeds to make a decision of adding cashers he needs to consider manyfactors including the number of available cashers at a particular time,labor cost of each casher, cost of spaces for adding checkout lines, andmany other factors. Any factor may impact the solution of the manager tomake an optimum solution. This example is a simple case which may beeasily analyzed and concluded by a human being. However, when theaffecting factors become huge, we must rely on consolidated artificialintelligence to analyze the collected data and rely on high speedprocessing system to conclude an optimum solution in a reasonable andacceptable time so that we can accommodate the changes. This inventioncollects data from a real life by knowing various affecting factors, andpartitions the data and group the data in a way it can be efficientlyretrieved. This invention sets up different models based on the collectdata so that it represents different characteristics of the real life.It analyzes the models (characteristics) to conclude an optimal modelthat represents the operation behavior of a real-life system, andassociated constraints. By obtaining an optimal model, an optimalsolution can be concluded based on known and desired operationrequirements.

A more complete understanding of the present invention, as well asfurther features and advantages of the present invention, will beobtained by reference to the following drawings and detail descriptions.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a system overview of this invention.

FIG. 2 is a detail process flow of the Data Modeler and Data Analyzer

DETAIL DESCRIPTIONS OF THE INVENTION Terminology and Lexicography

Plurality types of data: Data of different characteristic meaning. Forexample, but not limited to, number of calls within a period of time,radio frequency usage, data accessing rules, network topologies, etc.

Modeling: A process of generating an abstract model that usesmathematical language to describe the behavior of a system.

Replicate-Tree Indexing: A method of indexing Replicate-tree structures.

Hash Code Indexing: In computer science, a hash table, or a hash map, isa data structure that associates keys with values. The primary operationit supports efficiently is a lookup: given a key (e.g. a person's name),find the corresponding value (e.g. that person's telephone number). Itworks by transforming the key using a hash function into a hash, anumber that the hash table uses to locate the desired value.Reverse Indexing: A database index that uses the reversal of the keyvalues rather than the values themselves.Suffix Indexing: An indexing method for a suffix tree of a string S of ncharacters containing all n suffixes of S. It is a substring index.Average Value: Arithmetic mean.Normal Distribution: The normal distribution, also called Gaussiandistribution, is a probability distribution. It is a family ofdistributions of the same general form, differing in their location andscale parameters: the mean (“average”) and standard deviation(“variability”), respectively. The standard normal distribution is thenormal distribution with a mean of zero and a standard deviation of one.It is often called the bell curve because the graph of its probabilitydensity resembles a bell.Common Occurrence: A statistic and normalization algorithm bydynamically adjusting normalization criteria according to distributionof distinct values of normalized data.Non-common Occurrence: A statistic and normalization algorithm bycomparing original input to distribution of distinct values ofnormalization result.Finite Impulse Response (FIR) Algorithm: An algorithm for modeling byselecting the coefficients such that the system has specificcharacteristics.Infinite Impulse Response (IIR) Algorithm: An algorithm for modelingwith impulse response function which is non-zero over an infinite lengthof time.Vectorization Algorithm: Vectorization, is a process of converting analgorithm from a scalar implementation, which does an operation one pairof operands at a time, to a vector process where a single instructioncan refer to a vector (series of adjacent values).Fast Fourier Transform (FFT) Algorithm: A fast Fourier transformalgorithm is an algorithm to compute the discrete Fourier transform(DFT) and its inverse. FFTs.Z-Transform Algorithm: The Z-transform converts a discrete time domainsignal, which is a sequence of real numbers, into a complex frequencydomain representation.Differential Run-length Huffman Algorithm: A data compression andcharacteristic analysis algorithm by calculating differences andcontinuity of a data sequence.Down Sampling Algorithm: A process of reducing the sampling rate. Thisis usually done to reduce the data rate or the size of the data.Geometrical Prediction Algorithm: An algorithm discovering relationshipsof a multi-dimension data set by comparing prediction and original data.Boundary Algorithm: A modeling process by identifying boundary oftopological spaces that allow one to formalize concepts such asconvergence, connectedness and continuity.Hierarchical Linear Modeling (HLM) Algorithm: Also known as multi-levelanalysis, is a more advanced form of simple linear regression andmultiple linear regression. HLM allows variance in outcome variables tobe analyzed at multiple hierarchical levels.Singular Value Decomposition Algorithm: singular value decomposition(SVD) is a factorization of a rectangular real or complex matrix, withseveral applications in signal processing and statistics.Vertical Vectorization Algorithm: A vectorization along with thevertical dimension.Horizontal Grouping Algorithm: A grouping process along with thehorizontal dimension.Gravity-Velocity Analysis Algorithm: An algorithm partitioning amulti-dimension data set into groups and sub-domains by calculatingpredictability and continuity.Bayesian Analysis Algorithm: Bayesianism is the tenet that themathematical theory of probability is applicable to the degree to whicha person believes a proposition. Bayesians also hold that Bayes' theoremcan be used as the basis for a rule for updating beliefs in the light ofnew information—such updating is known as Bayesian inference.Bayesianism is an application of the probability calculus and aprobability interpretation of the term probable, or—as it is usuallyput—an interpretation of probability.Error Diffuse Iteration Algorithm: An algorithm for solving relationsbetween different data sets by prediction and error feedback.Constraints: Events, conditions, or rules, in a form of mathematicalrepresentations, that limit expected outcome from a task.Pattern-Relationship: Representing relations between groups of data bydefining N factors with weights each as pattern and M factors withweights as their relationships.Vendor Independent Model: Representing an implementation independentmodel of a multiple nodes system by defining inputs, outputs andstimulation-reaction pattern between input and output.

The present information is a system that provides capabilities ofcollecting data, partitioning data, modeling, and analyzing createdmodels in accordance of various intelligence. There are five majormodules namely, data collector, data partitioner, data modeler, modelanalyzer, and optimizer.

The data collector collects user data from real life operation behaviorthat are required for analysis. For example, in a wireless networkenvironment the user data may include, but not limited to, radiofrequency data, performance counter, environment definition, andhardware rules. For a national security command control channel, theuser data may include, but not limited to, legs of command channel,delay of command issuance, rules of command communication, communicationhardware constraints, and resource availability. The user data arecollected through a predefined time of period so that these datarepresent a real-life historical data. When these data were collected,it includes timing and situations when both normal and abnormal eventsoccurred. For example, the performance counter in a network environmentincludes the performance data of various circuit cards collected withina period of time. The “delay of a command issuance” in a command controlchannel includes the delay data that are collected through a period oftime. Therefore, the data collected represent what have happened in areal life of a real environment.

The data partitioner partitions the collected user data in accordance ofdata type and predefined partition rules. User data are partitioned in away such that related data can be grouped together for modeling process.The data partitioner further creates indices to indicate the groupeddata or particular data in a group. For example, the grouped data may begrouped in plurality of data files. The indices may indicate locationsor the names of data files. The indices may also indicate locations ofparticular data elements within data files. The process of partitioning,grouping, and indexing are for the ease of retrieving data by the systemfor creating various models. There are many indexing methods known toone skilled in the art. Every indexing method has its own purpose andadvantages. This invention implements indexing methods includingreplicate tree indexing, hash code indexing, reverse indexing, andsuffix indexing. Other indexing methods may also be implementeddepending on the nature and characteristics of an operating system andenvironment where various types of data are collected.

The data modeler generates various models by using the partitioned dataand predefined modeling rules. The predefined modeling rules areadditional rules specific to the modeling environment. For example,network topology, equipment configurations, and network design rules arespecific modeling rules for the network environment. For a commandcontrol channel environment, the command level, command authority, andcommand topology are specific modeling rules for the data modeler. Thedata modeler takes partitioned data as input and uses various modelingalgorithms to generate different models. The generated models representdifferent characteristics of the real-life operation environment wherethe user data were collected. These models present both normality andabnormalities of operation events from the collected data. The modelspresent what events caused the abnormalities in view of any otherfactors. For example, in a network environment, the models may present asurge of communication traffic at a specific time. The models may alsopresent occurring frequency of any abnormalities. The modeling processis an intensive mathematical calculation and processing on the collecteddata. The outcome of the modeling process are models which arerepresented by strings of numbers. This invention implements modelingalgorithms including, but not limited to, Finite Impulse Response (FIR)algorithm, Infinite Impulse Response (IIR) algorithm, Segmentationalgorithm, Vectorization algorithm, Fast Fourier Transform (FFT)algorithm, Z-Transform algorithm, Differential Run-length Huffmanalgorithm, Down Sampling algorithm, Geometrical Prediction algorithm,Boundary algorithm, and Hierarchical Linear Modeling algorithm. Othermodeling algorithms that are known to one skilled in the art may beimplemented by this invention without departing the invention concept.

The data analyzer analyzes the models generated by the modeler inaccordance with predefined rules. For a network environment, thepredefined rules may be, but not limited to, design rules and equipmentconfiguration rules. The algorithms for data analyzer analyzing theplurality of models comprise Singular Value Decomposition algorithm,Vertical Vectorization algorithm, Vertical Grouping algorithm,Horizontal Grouping algorithm, Gravity-Velocity Analysis algorithm,Bayesian Analysis algorithm, Error Diffuse Iteration algorithm togenerate Vendor Independent Models, and Pattern-Relationship. Theresults of the analysis performed by the data analyzer include anOptimal Model that best represents the operation system where theoperation data were collected.

The Optimizer provides a tool to conclude an optimal solution based onthe optimal model and various desired operation requirements. Theoptimal solution indicates the desired performance of the operationsystem in accordance with changing system requirement. Theses systemrequirements are various affecting factors that determine how the systemperform. In a wireless network environment, the system requirements maybe vendor specific equipment limitations, network topology limitations,radio frequency resource limitations, traffic load requirements, etc.For a command control system, the system requirements may be legs ofcommand channel path limitations, command authority limitations andrequirements, command issuance and response requirements, etc.

Embodiment 1

In a wireless network, the growth of traffic either due to subscribersdemands or special events have been significant issues for a servicecarrier to concern their network's performance. There are many factorsand network elements to be considered when upgrading or reconfiguringthe network in order to meet the performance requirements. The factorsand elements include radio frequency (RF) resources, network topology,hardware elements, design rules, equipment vendor specific constraints,etc. All performance data from the network can be collected through timefor analysis. However, these raw data, not only because its huge amountbut also have no correlation between each other, are beyond humanbeing's capability to analyze and conclude with an optimal solution inan accurate manner.

This invention first collects Environment Definitions, Hard Rules,Performance Counter, and RF Data that either have been collected througha period of time from live traffic or are predefined rules. The DataPartitioner partitions these data in a way that related data can begrouped together. The Data Partitioner generates data files and indices.The data files comprise partitioned data and the indices indicate thedata files and specific data within the data files. This partitioningand grouping process is an essential preparation in order to efficientlyaccess the collected raw data for modeling.

After partitioning and grouping performed by the Data Partitioner, thepartitioned data files are input to the Data Modeler. The Data Modeler,in response of receiving the data files, generates plurality of modelsin accordance with various predefined rules. These predefined rulesinclude Design Rules, Network Topology, and Equipment ConfigurationRules. The modeling processes, based on different modeling algorithms,are a series of mathematical computations. The results, in formats ofmathematical representations, are the models that present differentoperation characteristics of the collected real-life data. The operationcharacteristics include, for example, peaks of traffic occurred,frequency of the peaks of traffic occurred within a specified period oftime, load of particular network elements, etc. By having these models,the service carrier can better realize how the network performed in viewof the planned performance goals.

The Model Analyzer, in response of receiving the models, analyzes thesemodels in accordance with the predefined Design Rules and EquipmentConfiguration Rules and generates Intermediate Optimal Models. Theanalysis starts with predefined Constraints and, based on theConstraints, an Intermediate Optimal Model is created. By adjusting theConstraints, the Data Analyzer repeats the analysis and concluded withan Optimal Model. An Optimizer, in accordance with the Optimal Model anddesired system requirements, generates a performance solution. Theanalysis performed by the Data Analyzer and the optimization performedby the Optimizer are repeated until the performance solution reaches apredefined range. The final Intermediate Optimal Model is an OptimalModel representing the operation system and the final performancesolution is the optimal solution of the system based on the desiredperformance requirements and system constraints.

Embodiment 2

In a command control environment, the command traffic are routinelytracked and recorded. The traffic data includes, but not limited to,volume of command instructions, types of commands, time delay of commandissuance, time delay of each command process at each hub along thecommand channel, authority level of each command hub, command hubtopology, hardware limitations of command channels.

The collected real-life data are input to the Data Partitioner and are,then, partitioned and grouped. Associated indices are also generated toindicate the grouped data after partitioning as well as to indicateparticular data located within particular data groups. The partitioningand grouping process performed by the Data Partitioner is to efficientlyorganizing related data for the ease of access by the Data Modeler.

The partitioned and grouped data are then input to the Data Modeler forgenerating various models in order to present different systemcharacteristics obtained from the real-life data collection. By usingmathematical modeling algorithms, the models are represented indifferent mathematical expressions. The models may present significantcommand delays at a particular time period, delays due to lack ofauthority level, delays due to hardware or topology limitations.Therefore, any outstanding characteristic, either normal or abnormal,can be identified by these models.

The models are further analyzed by the Model Analyzer to generateoptimum solutions for improving the command control system performanceunder specified Constraints. The Constraints can be dynamicallygenerated for recursive analysis processes until an optimum solution isconcluded within a predefined value.

It is to be understood that the embodiments and variations shown anddescribed herein are merely illustrative of the principles of thisinvention and that various modifications may be implemented by thoseskilled in the art without departing from the scope and spirit of theinvention.

1. A computer-readable medium having computer-executable instructionsfor analysis of a wireless network, wherein said instructionscomprising: instructing a data partitioner to receive a first set ofplurality types of data, wherein the data partitioner partitions thefirst set of data and groups the partitioned data into a plurality ofdata files, and the data partitioner generates indices according to thefirst set of data; and instructing a data modeler to receive a secondset of plurality types of data, wherein the data modeler generates aplurality of models according to the first set of data and the secondset of data.
 2. The computer-readable medium having computer-executableinstructions for analysis of a wireless network of claim 1, wherein saidinstructions comprising: instructing a model analyzer to receive a thirdset of plurality types of data wherein the model analyzer analyzes theplurality of models according to the third set of plurality types ofdata.
 3. The computer-readable medium having computer-executableinstructions for analysis of a wireless network of claim 2, wherein theindices indicate the data files and the partitioned data in the datafiles.
 4. The computer-readable medium having computer-executableinstructions for analysis of a wireless network of claim 3, wherein theindices are generated by using indexing algorithms comprising of R-TreeIndexing, Hash Code Indexing, Reverse Indexing, and Suffix Indexing. 5.The computer-readable medium having computer-executable instructions foranalysis of a wireless network of claim 2, wherein said instructionscomprising: instructing the data partitioner to partition the first setof data by selecting an Average value of the first set of data, and byselecting data from eighty percent of normal distribution of the firstset of data, and by selecting data from twenty percent of the first setof data wherein the twenty percent of the first set of data were notselected by the selection of the eighty percent of the normaldistribution of the first set of data, and by selecting data of maximumvalues and minimum values from the first set of data, and by selectingdata from common occurrence data of the first set of data, and byselecting data from non-common occurrence data of the first set of data.6. The computer-readable medium having computer-executable instructionsfor analysis of a wireless network of claim 5, wherein the plurality ofmodels are generated by Finite Impulse Response (FIR) algorithm, andInfinite Impulse Response (IIR) algorithm, and Segmentation algorithm,and Vectorization algorithm, and Fast Fourier Transform (FFT) algorithm,and Z-Transform algorithm, and Differential Run-length Huffmanalgorithm, and Down Sampling algorithm, and Geometrical Predictionalgorithm, and Boundary algorithm, and Hierarchical Linear Modelingalgorithm.
 7. The computer-readable medium having computer-executableinstructions for analysis of a wireless network of claim 6, wherein saidinstructions comprising: instructing the model analyzer to analyze theplurality of models by using Singular Value Decomposition (SVD)algorithm, and Vertical Vectorization algorithm, and Vertical Groupingalgorithm, and Horizontal Grouping algorithm, and Gravity-VelocityAnalysis algorithm, and Bayesian Analysis algorithm, and Error DiffuseIteration algorithm to generate an optimal model.
 8. Thecomputer-readable medium having computer-executable instructions foranalysis of a wireless network of claim 7, wherein said instructionscomprising: instructing the model analyzer to generate DynamicConstraints; and instructing the model analyzer to generate modifiedoptimal models by new Dynamic Constraints.
 9. The computer-readablemedium having computer-executable instructions for analysis of awireless network of claim 8, wherein the instructions comprising:instructing an Optimizer, in accordance with the modified optimalmodels, to generate an optimal system performance solution.
 10. Acomputer-readable medium having computer-executable instructions foranalysis of a wireless network, wherein said instructions comprising:instructing a data partitioner to receive a first set of plurality typesof data, wherein the data partitioner partitions the first set of dataand groups the partitioned data into a plurality of data files, and thedata partitioner generates indices according to the first set of data;and instructing a model analyzer to receive a second set of data andgenerate optimal models and Dynamic Constraints.
 11. Thecomputer-readable medium having computer-executable instructions foranalysis of a wireless network of claim 10, wherein said instructionscomprising, instructing a data modeler to receive a third set ofplurality types of data, wherein the data modeler generates a pluralityof models according to the first set of data and the third set of data.12. The computer-readable medium having computer-executable instructionsfor analysis of a wireless network of claim 11, wherein the indicesindicate data files and the partitioned data in the data files, and theindices are generated by using replicate tree indexing, hash codeindexing, reverse indexing, and suffix indexing.
 13. Thecomputer-readable medium having computer-executable instructions foranalysis of a wireless network of claim 11, wherein said instructioncomprising: instructing the data partitioner to partition the first setof data by selecting an Average of the first set of data, and byselecting data from eighty percent of normal distribution of the firstset of data, and by selecting data from twenty percent of the first setof data wherein the twenty percent of the first set of data were notselected by the selection of the eighty percent of the normaldistribution of the first set of data, and by selecting data of maximumvalues and minimum values from the first set of data, and by selectingdata from common occurrence data of the first set of data, and byselecting data from non-common occurrence data of the first set of data.14. The computer-readable medium having computer-executable instructionsfor analysis of a wireless network of claim 13, wherein the plurality ofmodels are generated by Finite Impulse Response (FIR) algorithm, andInfinite Impulse Response (IIR) algorithm, and Segmentation algorithm,and Vectorization algorithm, and Fast Fourier Transform (FFT) algorithm,and Z-Transform algorithm, and Differential Run-length Huffmanalgorithm, and Down Sampling algorithm, and Geometrical Predictionalgorithm, and Boundary algorithm, and Hierarchical Linear Modelingalgorithm.
 15. The computer-readable medium having computer-executableinstructions for analysis of a wireless network of claim 14, whereinsaid instructions comprising: instructing the model analyzer to analyzethe plurality of models by using Singular Value Decomposition (SVD)algorithm, and Vertical Vectorization algorithm, and Vertical Groupingalgorithm, and Horizontal Grouping algorithm, and Gravity-VelocityAnalysis algorithm, and Bayesian Analysis algorithm, and Error DiffuseIteration algorithm to generate optimal models.
 16. Thecomputer-readable medium having computer-executable instructions foranalysis of a wireless network of claim 15, wherein said instructionscomprising: instructing the model analyzer generates optimal models byDynamic Constraints; and instructing an Optimizer to generate an optimalperformance solution based on one of the optimal models.
 17. Acomputer-readable medium having computer-executable instructions foranalysis of a wireless network, wherein said instructions comprising:instructing a data partitioner to receive a first set of plurality typesof data, wherein the data partitioner partitions the first set of dataand groups the partitioned data into plurality of data files, and thedata partitioner generates indices according to the first set of data;instructing a data modeler to receive a second set of plurality types ofdata, wherein the data modeler generates a plurality of models accordingto the first set of data and the second set of data; and instructing amodel analyzer to receive a third set of data wherein the model analyzeranalyzes the plurality of models according to the third set of data. 18.The computer-readable medium having computer-executable instructions foranalysis of a wireless network of claim 17, wherein the indices indicatedata files and the partitioned data in the data files; and the indicesare generated by using replicate tree indexing, hash code indexing,reverse indexing, and suffix indexing.
 19. The computer-readable mediumhaving computer-executable instructions for analysis of a wirelessnetwork of claim 18, wherein said instructions comprising: instructingthe data partitioner to partition the first set of data by selecting anAverage of the first set of data, and by selecting data from eightypercent of normal distribution of the first set of data, and byselecting data from twenty percent of the first set of data wherein thetwenty percent of the first set of data were not selected by theselection of the eighty percent of the normal distribution of the firstset of data, and by selecting data of maximum values and minimum valuesfrom the first set of data, and by selecting data from common occurrencedata of the first set of data, and by selecting data from non-commonoccurrence data of the first set of data; and the plurality of modelsare generated by Finite Impulse Response (FIR) algorithm, and InfiniteImpulse Response (IIR) algorithm, and Segmentation algorithm, andVectorization algorithm, and Fast Fourier Transform (FFT) algorithm, andZ-Transform algorithm, and Differential Run-length Huffman algorithm,and Down Sampling algorithm, and Geometrical Prediction algorithm, andBoundary algorithm, and Hierarchical Linear Modeling algorithm.
 20. Thecomputer-readable medium having computer-executable instructions foranalysis of a wireless network of claim 19, wherein said instructionscomprising: instructing the model analyzer to analyze the plurality ofmodels by using Singular Value Decomposition (SVD) algorithm, andVertical Vectorization algorithm, and Vertical Grouping algorithm, andHorizontal Grouping algorithm, and Gravity-Velocity Analysis algorithm,and Bayesian Analysis algorithm, and Error Diffuse Iteration algorithmto generate Vendor Independent Models, and Pattern-Relationship;instructing the model analyzer to generate optimal models and DynamicConstraints; and instructing an Optimizer to generate an optimalsolution based on one of the optimal models.